Class 10: Halloween Mini Project
Ashley Vang
10/27/2022
Importing Candy Data
First save the input into your project directory.
candy_file <- "candy-data.csv"Then load and input the data.
candy= read.csv(candy_file, row.names=1)
head(candy)## chocolate fruity caramel peanutyalmondy nougat crispedricewafer
## 100 Grand 1 0 1 0 0 1
## 3 Musketeers 1 0 0 0 1 0
## One dime 0 0 0 0 0 0
## One quarter 0 0 0 0 0 0
## Air Heads 0 1 0 0 0 0
## Almond Joy 1 0 0 1 0 0
## hard bar pluribus sugarpercent pricepercent winpercent
## 100 Grand 0 1 0 0.732 0.860 66.97173
## 3 Musketeers 0 1 0 0.604 0.511 67.60294
## One dime 0 0 0 0.011 0.116 32.26109
## One quarter 0 0 0 0.011 0.511 46.11650
## Air Heads 0 0 0 0.906 0.511 52.34146
## Almond Joy 0 1 0 0.465 0.767 50.34755Q1. How many different candy types are in this dataset?
nrow(candy)## [1] 85There are 85 different types of candy.
Q2. How many fruity candy types are in the dataset?
sum(candy$fruity)## [1] 38There are 38 fruity candy types in the dataset.
We can find the winpercent value for Twix. (Those with a higher value people like more)
What is your favorite candy?
rownames(candy)## [1] "100 Grand" "3 Musketeers"
## [3] "One dime" "One quarter"
## [5] "Air Heads" "Almond Joy"
## [7] "Baby Ruth" "Boston Baked Beans"
## [9] "Candy Corn" "Caramel Apple Pops"
## [11] "Charleston Chew" "Chewey Lemonhead Fruit Mix"
## [13] "Chiclets" "Dots"
## [15] "Dum Dums" "Fruit Chews"
## [17] "Fun Dip" "Gobstopper"
## [19] "Haribo Gold Bears" "Haribo Happy Cola"
## [21] "Haribo Sour Bears" "Haribo Twin Snakes"
## [23] "HersheyÕs Kisses" "HersheyÕs Krackel"
## [25] "HersheyÕs Milk Chocolate" "HersheyÕs Special Dark"
## [27] "Jawbusters" "Junior Mints"
## [29] "Kit Kat" "Laffy Taffy"
## [31] "Lemonhead" "Lifesavers big ring gummies"
## [33] "Peanut butter M&MÕs" "M&MÕs"
## [35] "Mike & Ike" "Milk Duds"
## [37] "Milky Way" "Milky Way Midnight"
## [39] "Milky Way Simply Caramel" "Mounds"
## [41] "Mr Good Bar" "Nerds"
## [43] "Nestle Butterfinger" "Nestle Crunch"
## [45] "Nik L Nip" "Now & Later"
## [47] "Payday" "Peanut M&Ms"
## [49] "Pixie Sticks" "Pop Rocks"
## [51] "Red vines" "ReeseÕs Miniatures"
## [53] "ReeseÕs Peanut Butter cup" "ReeseÕs pieces"
## [55] "ReeseÕs stuffed with pieces" "Ring pop"
## [57] "Rolo" "Root Beer Barrels"
## [59] "Runts" "Sixlets"
## [61] "Skittles original" "Skittles wildberry"
## [63] "Nestle Smarties" "Smarties candy"
## [65] "Snickers" "Snickers Crisper"
## [67] "Sour Patch Kids" "Sour Patch Tricksters"
## [69] "Starburst" "Strawberry bon bons"
## [71] "Sugar Babies" "Sugar Daddy"
## [73] "Super Bubble" "Swedish Fish"
## [75] "Tootsie Pop" "Tootsie Roll Juniors"
## [77] "Tootsie Roll Midgies" "Tootsie Roll Snack Bars"
## [79] "Trolli Sour Bites" "Twix"
## [81] "Twizzlers" "Warheads"
## [83] "WelchÕs Fruit Snacks" "WertherÕs Original Caramel"
## [85] "Whoppers"candy["Twix", ]$winpercent## [1] 81.64291Q3. What is your favorite candy in the dataset and what is it’s winpercent value?
candy["Sour Patch Kids",]$winpercent## [1] 59.864My favorite candy is sour patch kids. Its winpercent value is 59.864.
Q4. What is the winpercent value for “Kit Kat”?
candy["Kit Kat",]$winpercent## [1] 76.7686The winpercent for kit kat is 76.7686.
Q5. What is the winpercent value for “Tootsie Roll Snack Bars”?
candy["Tootsie Roll Snack Bars",]$winpercent## [1] 49.6535The winpercent value for tootsie roll snack bars is 49.6535
We can look at the overview of the given dataset.
library("skimr")
skim(candy)| Name | candy |
| Number of rows | 85 |
| Number of columns | 12 |
| _______________________ | |
| Column type frequency: | |
| numeric | 12 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| chocolate | 0 | 1 | 0.44 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| fruity | 0 | 1 | 0.45 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| caramel | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| peanutyalmondy | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| nougat | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| crispedricewafer | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| hard | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| bar | 0 | 1 | 0.25 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| pluribus | 0 | 1 | 0.52 | 0.50 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| sugarpercent | 0 | 1 | 0.48 | 0.28 | 0.01 | 0.22 | 0.47 | 0.73 | 0.99 | ▇▇▇▇▆ |
| pricepercent | 0 | 1 | 0.47 | 0.29 | 0.01 | 0.26 | 0.47 | 0.65 | 0.98 | ▇▇▇▇▆ |
| winpercent | 0 | 1 | 50.32 | 14.71 | 22.45 | 39.14 | 47.83 | 59.86 | 84.18 | ▃▇▆▅▂ |
Q6. Is there any variable/column that looks to be on a different scale to the majority of the other columns in the dataset?
The histogram column that plots the winpercent looks to be on a different scale. It is not on a 0 to 1 scale or out of 100 scale, it is not on a numerical scale.
Q7. What do you think a zero and one represent for the candy$chocolate column?
candy$chocolate## [1] 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 1 1 0 0 0 1 1 0 1 1 1
## [39] 1 1 1 0 1 1 0 0 0 1 0 0 0 1 1 1 1 0 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1
## [77] 1 1 0 1 0 0 0 0 1A 1 means there is chocolate in that candy and a 0 means there is no chocolate in the candy.
Q8. Plot a histogram of winpercent values
We can plot using two different methods. hist() or ggplot().
hist(candy$winpercent)
library(ggplot2)
ggplot(candy) +
aes(winpercent) +
geom_histogram(bins=10, col="red", fill="orange")
Q9. Is the distribution of winpercent values symmetrical?
No, the distribution is not symmetrical, you can see the values are unevenly distributed.
Q10. Is the center of the distribution above or below 50%?
The center of distribution is above 50%.
Q11. On average is chocolate candy higher or lower ranked than fruit candy?
chocolate.inds <-as.logical(candy$chocolate)
chocolatewins <-candy[chocolate.inds,]$winpercent
mean(candy[chocolate.inds,]$winpercent)## [1] 60.92153fruity.inds <- as.logical(candy$fruity)
fruitwins <-candy[fruity.inds,]$winpercent
mean(candy[fruity.inds,]$winpercent)## [1] 44.11974On average the chocolate candy is higher ranked than the fruity candy.
Q12. Is this difference statistically significant?
t.test(chocolatewins, fruitwins)##
## Welch Two Sample t-test
##
## data: chocolatewins and fruitwins
## t = 6.2582, df = 68.882, p-value = 2.871e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 11.44563 22.15795
## sample estimates:
## mean of x mean of y
## 60.92153 44.11974There is a statistically significant difference between the two as seen from the low p-value.
Overall Candy Rankings
Q13. What are the five least liked candy types in this set?
head(candy[order(candy$winpercent),], n=5)## chocolate fruity caramel peanutyalmondy nougat
## Nik L Nip 0 1 0 0 0
## Boston Baked Beans 0 0 0 1 0
## Chiclets 0 1 0 0 0
## Super Bubble 0 1 0 0 0
## Jawbusters 0 1 0 0 0
## crispedricewafer hard bar pluribus sugarpercent pricepercent
## Nik L Nip 0 0 0 1 0.197 0.976
## Boston Baked Beans 0 0 0 1 0.313 0.511
## Chiclets 0 0 0 1 0.046 0.325
## Super Bubble 0 0 0 0 0.162 0.116
## Jawbusters 0 1 0 1 0.093 0.511
## winpercent
## Nik L Nip 22.44534
## Boston Baked Beans 23.41782
## Chiclets 24.52499
## Super Bubble 27.30386
## Jawbusters 28.12744The five least liked candy types are Nik L Nip, Boston Baked Beans, Chiclets, Super Bubble, and Jawbusters.
Q14. What are the top 5 all time favorite candy types out of this set?
tail(candy[order(candy$winpercent),], n=5)## chocolate fruity caramel peanutyalmondy nougat
## Snickers 1 0 1 1 1
## Kit Kat 1 0 0 0 0
## Twix 1 0 1 0 0
## ReeseÕs Miniatures 1 0 0 1 0
## ReeseÕs Peanut Butter cup 1 0 0 1 0
## crispedricewafer hard bar pluribus sugarpercent
## Snickers 0 0 1 0 0.546
## Kit Kat 1 0 1 0 0.313
## Twix 1 0 1 0 0.546
## ReeseÕs Miniatures 0 0 0 0 0.034
## ReeseÕs Peanut Butter cup 0 0 0 0 0.720
## pricepercent winpercent
## Snickers 0.651 76.67378
## Kit Kat 0.511 76.76860
## Twix 0.906 81.64291
## ReeseÕs Miniatures 0.279 81.86626
## ReeseÕs Peanut Butter cup 0.651 84.18029The top 5 favorite candy types are Snickers, Kit kats, twix, ReeseOs minis, and ReeseOs Peanut Butter Cup.
Q15. Make a first barplot of candy ranking based on winpercent values.
library(ggplot2)
ggplot(candy) +
aes(winpercent,rownames(candy)) +
geom_col()
Q16. This is quite ugly, use the reorder() function to get the bars sorted by winpercent?
library(ggplot2)
ggplot(candy) +
aes(winpercent, reorder(rownames(candy),winpercent)) +
geom_col()
my_cols=rep("black", nrow(candy))
my_cols[as.logical(candy$chocolate)] = "yellow"
my_cols[as.logical(candy$bar)] = "brown"
my_cols[as.logical(candy$fruity)] = "pink"
#my_colsggplot(candy) +
aes(winpercent, reorder(rownames(candy),winpercent)) +
geom_col(fill=my_cols) 
Q17. What is the worst ranked chocolate candy?
The worst ranked chocolate candy is sixlets.
Q18. What is the best ranked fruity candy?
The best ranked fruity candy is starburst.
Taking a look at pricepercent
We can plot the value for money per each candy. To see which is best in terms of price and win percent.
library(ggrepel)
# How about a plot of price vs win
ggplot(candy) +
aes(winpercent, pricepercent, label=rownames(candy)) +
geom_point(col=my_cols) +
geom_text_repel(col=my_cols, size=3.3, max.overlaps = 10)## Warning: ggrepel: 10 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
>Q19. Which candy type is the highest ranked in terms of winpercent for the least money - i.e. offers the most bang for your buck?
The highest rank in terms of winpercent for the least money is Reese O’s minis.
Q20. What are the top 5 most expensive candy types in the dataset and of these which is the least popular?
ord <- order(candy$pricepercent, decreasing = TRUE)
head( candy[ord,c(11,12)], n=5 )## pricepercent winpercent
## Nik L Nip 0.976 22.44534
## Nestle Smarties 0.976 37.88719
## Ring pop 0.965 35.29076
## HersheyÕs Krackel 0.918 62.28448
## HersheyÕs Milk Chocolate 0.918 56.49050The top 5 most expensive are Nik L Nip, Nestle Smarties, Ring pop, HersheyOs Krackel, and HersheyOs Milk Chocolate. The least popular amongst these are Nik L Nip.
Q21. Make a barplot again with geom_col() this time using pricepercent and then improve this step by step, first ordering the x-axis by value and finally making a so called “dot chat” or “lollipop” chart by swapping geom_col() for geom_point() + geom_segment().
# Make a lollipop chart of pricepercent
ggplot(candy) +
aes(pricepercent, reorder(rownames(candy), pricepercent)) +
geom_segment(aes(yend = reorder(rownames(candy), pricepercent),
xend = 0), col="gray40") +
geom_point()
Exploring the correlation structure
We will plot the correlation by using corrplot().
library(corrplot)## corrplot 0.92 loadedcij <- cor(candy)
corrplot(cij)
Q22. Examining this plot what two variables are anti-correlated (i.e. have minus values)?
The two variables that are anti-correlated are shown by the red colored dot which are chocolate and fruity.
Q23. Similarly, what two variables are most positively correlated?
Variables most positively correlated are winpercent (popularity) and chocolate.
Principal Component Analysis
Lets apply PCA.
pca <- prcomp(candy, scale=TRUE)
summary(pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.0788 1.1378 1.1092 1.07533 0.9518 0.81923 0.81530
## Proportion of Variance 0.3601 0.1079 0.1025 0.09636 0.0755 0.05593 0.05539
## Cumulative Proportion 0.3601 0.4680 0.5705 0.66688 0.7424 0.79830 0.85369
## PC8 PC9 PC10 PC11 PC12
## Standard deviation 0.74530 0.67824 0.62349 0.43974 0.39760
## Proportion of Variance 0.04629 0.03833 0.03239 0.01611 0.01317
## Cumulative Proportion 0.89998 0.93832 0.97071 0.98683 1.00000Now we can plot our PCA score plot, PC1 vs PC2.
plot(pca$x[,1:2])
plot(pca$x[,1:2], col=my_cols, pch=16)
We can also plot using ggplot.
# Make a new data-frame with our PCA results and candy data
my_data <- cbind(candy, pca$x[,1:3])p <- ggplot(my_data) +
aes(x=PC1, y=PC2,
size=winpercent/100,
text=rownames(my_data),
label=rownames(my_data)) +
geom_point(col=my_cols)
p
We can use ggrepel.
library(ggrepel)
p + geom_text_repel(size=3.3, col=my_cols, max.overlaps = 7) +
theme(legend.position = "none") +
labs(title="Halloween Candy PCA Space",
subtitle="Colored by type: chocolate bar (dark brown), chocolate other (light brown), fruity (red), other (black)",
caption="Data from 538")## Warning: ggrepel: 39 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
We can generate an interactive plot.
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutggplotly(p)par(mar=c(8,4,2,2))
barplot(pca$rotation[,1], las=2, ylab="PC1 Contribution")
Q24. What original variables are picked up strongly by PC1 in the positive direction? Do these make sense to you?
The original variables picked up by PC1 in the positive direction are fruity, hard, and pluribus. Yes, it makes sense because thats what most fruity candies are and fall under.